Evaluation and Optimization of Virtual Screening Workflows with DEKOIS 2.0 – A Public Library of Challenging Docking Benchmark Sets

We report an investigation designed to explore alternative approaches for ranking of docking poses in the search for antagonists of the adenosine A2A receptor, an attractive target for structure-based virtual screening. Calculation of 3D similarity of docking poses to crystallographic ligand(s) as well as similarity of receptor-ligand interaction patterns was consistently superior to conventional scoring functions for prioritizing antagonists over decoys. Moreover, the use of crystallographic antagonists and agonists, a core fragment of an antagonist, and a model of an agonist placed into the binding site of an antagonist-bound form of the receptor resulted in a significant early enrichment of antagonists in compound rankings. Taken together, these findings showed that the use of binding modes of agonists and/or antagonists, even if they were only approximate, for similarity assessment of docking poses or comparison of interaction patterns increased the odds of identifying new active compounds over conventional scoring.

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“…A benchmark set for the A 2A receptor was extracted from the DEKOIS 2.0 [30]. This set included 40 active compounds and 1200 decoys.…”

Section: Ligand Preparationmentioning

confidence: 99%

Binding mode similarity measures for ranking of docking poses: a case study on the adenosine A2A receptor

Anighoro

Bajorath

2016

J Comput Aided Mol Des

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“…To this end, individual groups have tried to optimize their original data sets so as to minimize all potential benchmarking biases [58, 65, 99]. In June 2012, Irwin and Shoichet released an enhanced version of DUD, DUD: Enhanced (DUD-E) for short [58], which addressed all the observed weaknesses in original DUD.…”

Section: Currently Available Benchmarking Setsmentioning

confidence: 99%

“…In 2013, Boeckler’s group also realized the weaknesses in DEKOIS, in particular its hidden analogue bias, thus updated DEKOIS to version 2.0 [65] by: (1) including 3 additional physicochemical properties for larger matching; (2) updating LADS score for avoidance of latent actives; and (3) clustering initial ligands for higher chemical diversity. These updates further reduced three main biases, i.e.…”

Section: Currently Available Benchmarking Setsmentioning

confidence: 99%

“…the SBVS-specific and the LBVS-specific. Datasets such as Directory of Useful Decoys (DUD) [57] and its recent DUD-Enhanced (DUD-E) [58], virtual decoy sets (VDS) [62], G protein-coupled receptors (GPCRs) ligand library (GLL) and GPCRs Decoy Database (GDD) [63], Demanding Evaluation Kits for Objective in Silico Screening (DEKOIS) [64] and DEKOIS 2.0 [65], Nuclear Receptors Ligands and Structures Benchmarking DataBase (NRLiSt BDB) belong to SBVS-specific benchmarking sets. By contrast, only 3 datasets, i.e.…”

Section: Introductionmentioning

confidence: 99%

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Benchmarking methods and data sets for ligand enrichment assessment in virtual screening

Xia

Tilahun

Reid

et al. 2015

Methods

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Retrospective small-scale virtual screening (VS) based on benchmarking data sets has been widely used to estimate ligand enrichments of VS approaches in the prospective (i.e. real-world) efforts. However, the intrinsic differences of benchmarking sets to the real screening chemical libraries can cause biased assessment. Herein, we summarize the history of benchmarking methods as well as data sets and highlight three main types of biases found in benchmarking sets, i.e. “analogue bias”, “artificial enrichment” and “false negative”. In addition, we introduced our recent algorithm to build maximum-unbiased benchmarking sets applicable to both ligand-based and structure-based VS approaches, and its implementations to three important human histone deacetylase (HDAC) isoforms, i.e. HDAC1, HDAC6 and HDAC8. The Leave-One-Out Cross-Validation (LOO CV) demonstrates that the benchmarking sets built by our algorithm are maximum-unbiased in terms of property matching, ROC curves and AUCs.

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“…In recent years, there has been an increasing number of benchmarking sets developed by multiple research groups worldwide, from DUD/DUD-E, MUV, to DEKOIS 2.0 (30)(31)(32)(33)(34)(35)(36)(37). Unfortunately, none of them provide available benchmarking data set for the particular TLR8 agonists.…”

mentioning

confidence: 99%

Enrichment Assessment of Multiple Virtual Screening Strategies for Toll‐Like Receptor 8 Agonists Based on a Maximal Unbiased Benchmarking Data Set

et al. 2015

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Toll-like receptor 8 agonists, which activate adaptive immune responses by inducing robust production of T-helper 1-polarizing cytokines, are promising candidates for vaccine adjuvants. As the binding site of toll-like receptor 8 is large and highly flexible, virtual screening by individual method has inevitable limitations; thus, a comprehensive comparison of different methods may provide insights into seeking effective strategy for the discovery of novel toll-like receptor 8 agonists. In this study, the performance of knowledge-based pharmacophore, shape-based 3D screening, and combined strategies was assessed against a maximum unbiased benchmarking data set containing 13 actives and 1302 decoys specialized for toll-like receptor 8 agonists. Prior structure-activity relationship knowledge was involved in knowledge-based pharmacophore generation, and a set of antagonists was innovatively used to verify the selectivity of the selected knowledge-based pharmacophore. The benchmarking data set was generated from our recently developed 'mubd-decoymaker' protocol. The enrichment assessment demonstrated a considerable performance through our selected three-layer virtual screening strategy: knowledge-based pharmacophore (Phar1) screening, shape-based 3D similarity search (Q4_combo), and then a Gold docking screening. This virtual screening strategy could be further employed to perform large-scale database screening and to discover novel toll-like receptor 8 agonists.

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Evaluation and Optimization of Virtual Screening Workflows with DEKOIS 2.0 – A Public Library of Challenging Docking Benchmark Sets

Cited by 142 publications

References 91 publications

Binding mode similarity measures for ranking of docking poses: a case study on the adenosine A2A receptor

Binding mode similarity measures for ranking of docking poses: a case study on the adenosine A2A receptor

Benchmarking methods and data sets for ligand enrichment assessment in virtual screening

Enrichment Assessment of Multiple Virtual Screening Strategies for Toll‐Like Receptor 8 Agonists Based on a Maximal Unbiased Benchmarking Data Set

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